Pascal Peter  
Position: Postdoctoral Researcher
(Akademischer Oberrat)
Office hour: Please contact me via mail
for online meetings.
Phone: +49-681-302-57361
E-mail: peter -at- mia.uni-saarland.de
(please replace anti-spam -at- by @)
Address: Mathematical Image Analysis Group
Faculty of Mathematics and Computer Science,
Saarland University
Campus E1.7
66041 Saarbrücken, Germany
Office: Room 4.01
Building E1.7, Saarbrücken Campus
see also Contact

Research – Publications – Teaching – Reviewing Activities


Research Areas

Publications

Journal Papers

  1. P. Peter, K. Schrader, T. Alt, J. Weickert:
    Deep spatial and tonal optimisation for homogeneous diffusion inpainting.
    Pattern Analysis and Applications, Vol. 26, No. 4, 1585-1600, November 2023.
    Invited Paper.
  2. T. Alt, K. Schrader, M. Augustin, P. Peter, J. Weickert:
    Connections between numerical algorithms for PDEs and neural networks.
    Journal of Mathematical Imaging and Vision, June 2022.
    Invited Paper.
    Also available as arXiv:2107.14742 [math.NA], revised March 2022.
  3. T. Alt, K. Schrader, J. Weickert, P. Peter, M. Augustin:
    Designing rotationally invariant neural networks from PDEs and variational methods.
    Research in the Mathematical Sciences, Vol. 9, No. 3, Article 52, Sept. 2022.
    Also available as arXiv:2108.13993 [cs.LG], revised March 2022.
  4. R. M. K. Mohideen, P. Peter, J. Weickert:
    A systematic evaluation of coding strategies for sparse binary images.
    Signal Processing: Image Communication, Vol. 99, Article 116424, November 2021.
    Also available as arXiv:2010.13634 [eess.IV], revised July 2021.
  5. M. Breuß, J. Buhl, A. M. Yarahmadi, M. Bambach, P. Peter:
    A simple approach to stiffness enhancement of a printable shape by Hamilton-Jacobi skeletonization.
    Procedia Manufacturing, Vol. 47, 1190-1196, 2020.
  6. L. Hoeltgen, P. Peter, M. Breuß:
    Clustering-Based Quantisation for PDE-Based Image Compression.
    Signal, Image and Video Processing, Vol. 12, No. 3, 411-419, Vol. 12, No. 3, 411-419 March 2018.
    Revised version of arXiv:1706.06347 [cs.CV], June 2017
  7. N. Amrani, J. Serra-Sagrista, P. Peter, J. Weickert:
    Diffusion-based inpainting for coding remote-sensing data.
    IEEE Geoscience and Remote Sensing Letters, Vol. 14, No. 8, 1203-1207, August 2017.
    Also available as Technical Report, Universitat Autonoma de Barcelona, Spain, March 2017, http://ddd.uab.cat/record/174184.
  8. P. Peter, L. Kaufhold, J. Weickert:
    Turning diffusion-based image colorization into efficient color compression.
    IEEE Transactions on Image Processing, Vol. 26, No. 2, 860-869, February 2017.
    Revised version of Technical Report No. 370, Department of Mathematics, Saarland University, Saarbrücken, Germany, December 2015.
  9. P. Peter, S. Hoffmann, F. Nedwed, L. Hoeltgen, J. Weickert:
    Evaluating the true potential of diffusion-based inpainting in a compression context.
    Signal Processing: Image Communication, Vol. 46, 40-53, August 2016.
    Revised version of Technical Report No. 373, Department of Mathematics, Saarland University, Saarbrücken, Germany, January 2016.
  10. P. Peter, C. Schmaltz, N. Mach, M. Mainberger, J. Weickert:
    Beyond Pure Quality: Progressive Modes, Region of Interest Coding, and Real Time Video Decoding for PDE-based Image Compression.
    Journal of Visual Communication and Image Representation, Vol. 31, 253-265, August 2015.
    Revised version of Technical Report No. 354, Department of Mathematics, Saarland University, Saarbrücken, Germany, January 2015.
  11. C. Schmaltz, P. Peter, M. Mainberger, F. Ebel, J. Weickert, A. Bruhn:
    Understanding, optimising, and extending data compression with anisotropic diffusion.
    International Journal of Computer Vision, Vol. 108, No. 3, 222-240, July 2014.
    Revised version of Technical Report No. 329, Department of Mathematics, Saarland University, Saarbrücken, Germany, March 2013.

    12. Book Chapters

  12. P. Peter and M. Breuß
    Refined Homotopic Thinning Algorithms and Quality Measures for Skeletonisation Methods.
    M. Breuß, A. Bruckstein, P. Maragos (Eds.): Innovations for Shape Analysis: Models and Algorithms. Mathematics and Visualization, 77-92, Springer, Berlin, 2013.
    Revised version of Technical Report No. 312, Department of Mathematics, Saarland University, Saarbrücken, Germany, July 2012.

    13. Conference Papers

  13. P. Bungert, P. Peter, J. Weickert:
    Image blending with osmosis.
    To appear in L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Springer, Cham, 2023.
  14. P. Peter:
    Generalised scale-space properties for probabilistic diffusion.
    To appear in L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Springer, Cham, 2023.
    Also available as arXiv:2303.07900 [eess.IV], March 2023.
  15. K. Schrader, P. Peter, N. Kämper, J. Weickert:
    Efficient neural generation of 4K masks for homogeneous diffusion inpainting.
    To appear in L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Springer, Cham, 2023.
  16. P. Peter:
    A Wasserstein GAN for joint learning of inpainting and its spatial optimisation.
    To appear in Proc. 10th Pacific-Rim Symposium on Image and Video Technology (PSIVT 2022, Online Event, Nov. 2022), Lecture Notes in Computer Science, Springer, Cham, 2022.
    Also available as arXiv:2202.05623 [eess.IV], February 2022.
  17. T. Alt, P. Peter, J. Weickert:
    Learning sparse masks for diffusion-based image inpainting.
    In A. J. Pinho, P. Georgieva, L. F. Teixeira, J. A. Sánchez (Eds.): Pattern Recognition and Image Analysis. Lecture Notes in Computer Science, Vol. 13256, Springer, Cham, 528-539, 2022.
    Also available as arXiv:2110.02636 [eess.IV], revised March 2022.
  18. S. Andris, J. Weickert, T. Alt, P. Peter:
    JPEG meets PDE-based image compression.
    In Proc. 35th Picture Coding Symposium (PCS 2021, Bristol, UK, June 2021), IEEE Press, 2021.
    Also available as arXiv:2011.11289 [eess.IV], revised May 2021.
  19. P. Peter:
    Quantisation scale-spaces.
    In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 12679, Springer, Cham, 15-26, 2021.
    Also available as arXiv:2103.10491 [eess.IV], March 2021.
  20. T. Alt, P. Peter, J. Weickert, K. Schrader:
    Translating numerical concepts for PDEs into neural architectures.
    In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 12679, Springer, Cham, 294-306, 2021.
    Also available as arXiv:2103.15419 [math.NA], March 2021.
  21. S. Andris, P. Peter, R. M. K. Mohideen, J. Weickert, S. Hoffmann:
    Inpainting-based video compression in FullHD.
    In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 12679, Springer, Cham, 425-436, 2021.
    Also available as arXiv:2008.10273 [eess.IV], revised May 2021.
  22. F. Jost, P. Peter, J. Weickert:
    Compressing piecewise smooth images with the Mumford-Shah cartoon model.
    In Proc. 28th European Signal Processing Conference (EUSIPCO 2020, Amsterdam, Netherlands, January 2021), 511-515, 2021.
    Also available as arXiv:2003.05206 [eess.IV], March 2020.
  23. F. Jost, P. Peter, J. Weickert:
    Compressing flow fields with edge-aware homogeneous diffusion inpainting.
    Proc. 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020, Barcelona, Spain, May 2020), 2198-2202, 2020.
    Also available as arXiv:1906.12263 [eess.IV], October 2019.
  24. P. Peter:
    Fast inpainting-based compression: Combining Shepard interpolation with joint inpainting and prediction.
    Proc. 2019 IEEE International Conference on Image Processing (ICIP 2019, Taipei, Taiwan, Sept. 2019), 3557-3561, 2019.
  25. M. Cárdenas, P. Peter, J. Weickert:
    Sparsification scale-spaces.
    In J. Lellmann, M. Burger, J. Modersitzki (Eds.): Scale Space and Variational Methods. Lecture Notes in Computer Science, Vol. 11603, 303-314, Springer, Cham, 2019.
  26. P. Peter, J. Contelly, J. Weickert:
    Compressing audio signals with inpainting-based sparsification.
    In J. Lellmann, M. Burger, J. Modersitzki (Eds.): Scale Space and Variational Methods. Lecture Notes in Computer Science, Vol. 11603, 92-103, Springer, Cham, 2019.
  27. L. Karos, P. Bheed, P. Peter, J. Weickert:
    Optimising data for exemplar-based inpainting.
    In J. Blanc-Talon, D. Helbert, W. Philips, D. Popescu, P. Scheunders (Eds.): Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science, Vol. 11182, 547-558, Springer, Cham, 2018.
  28. R. D. Adam, P. Peter, J. Weickert:
    Denoising by inpainting.
    In F. Lauze, Y. Dong, A. B. Dahl (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 10302, 121-132, Springer, Cham, 2017.
  29. S. Andris, P. Peter, J. Weickert:
    A proof-of-concept framework for PDE-based video compression.
    Proc. 32nd Picture Coding Symposium (PCS 2016, Nuremberg, Germany, December. 2016), 1-5, 2016.
    PCS 2016 Best Poster Award.
  30. M. Schneider, P. Peter, S. Hoffmann, J. Weickert, Enric Meinhardt-Llopis:
    Gradients versus grey values for sparse image reconstruction and inpainting-based compression.
    In J. Blanc-Talon, C. Distante, W. Philips, D. Popescu, P. Scheunders (Eds.): Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science, Vol. 10016, 1-13, Springer, Cham, 2016.
  31. P. Peter, S. Hoffmann, F. Nedwed, L. Hoeltgen, J. Weickert:
    From optimised inpainting with linear PDEs towards competitive image compression codecs.
    In T. Bräunl, B. McCane, M. Rivera, X. Yu (Eds.): Image and Video Technology. Lecture Notes in Computer Science, Vol. 9431, 63-74, Springer, Cham, 2016.
  32. P. Peter, J. Weickert:
    Compressing images with diffusion- and exemplar-based inpainting.
    In J.-F. Aujol, M. Nikolova, N. Papadakis (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 9087, 154-165, Springer, Berlin, 2015.
  33. P. Peter, J. Weickert, A. Munk, T. Krivobokova, H. Li:
    Justifying tensor-driven diffusion from structure-adaptive statistics of natural images.
    In X.-C. Tai, E. Bae, T. F. Chan, M. Lysaker (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Springer, Vol. 8932, 263-277, Berlin, 2015.
  34. P. Peter, J. Weickert:
    Colour image compression with anisotropic diffusion.
    Proc. 21st IEEE International Conference on Image Processing
    (ICIP 2014, Paris, France, October 2014), 4822-4826, 2014.
  35. P. Peter
    Three-dimensional data compression with anisotropic diffusion.
    J. Weickert, M. Hein, B. Schiele (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Volume 8142, 231-236, Springer, Berlin, 2013.

    36. Preprints

  36. D. Gaa, V. Chizhov, P. Peter, J. Weickert, R. D. Adam:
    Gaining Insights into Denoising by Inpainting.
    arXiv:2309.13486 [eess.IV], September 2023.
  37. P. Peter:
    Generalised Probabilistic Diffusion Scale-Spaces.
    arXiv:2309.08511 [eess.IV], September 2023.
  38. T. Alt, J. Weickert, P. Peter:
    Translating Diffusion, Wavelets, and Regularisation into Residual Networks.
    arXiv:2002.02753 [cs.LG], February 2020.
  39. T. Dahmen, P. Trampert, P. Peter, P. Bheed, J. Weickert, P. Slusallek:
    Space-Filling Curve Indices as Acceleration Structure for Exemplar-Based Inpainting.
    arXiv:1712.06326 [cs.CV], January 2020.

    40. Theses

  40. P. Peter
    Understanding and Advancing PDE-based Image Compression
    A Dissertation Submitted Towards the Degree Doctor of Engineering (Dr.-Ing.) of the Faculties of Natural Sciences and Technology of Saarland University, Saarbrücken, Germany, January 2016.

  41. P. Peter
    Kompression dreidimensionaler Daten mit anisotropen Diffusionsprozessen
    Thesis for High School Teachers, Dept. of Computer Science,
    Saarland University, Saarbrücken, Germany, January 2011.

  42. P. Peter
    Hamilton-Jacobi Skeletonisation in Image Processing
    Bachelor's Thesis in Computer Science, Dept. of Computer Science,
    Saarland University, Saarbrücken, Germany, February 2010.

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